Credit_card_Approval_prediction-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "OkEr_ywHxfDB",
    "outputId": "f2a76d19-e213-439a-ce61-05579864f128"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jG2vkeXfmUJz"
   },
   "source": [
    "### Install Java and Spark in Google Colab Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9oP9lNivxfyd"
   },
   "outputs": [],
   "source": [
    "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
    "!wget -q https://mirrors.estointernet.in/apache/spark/spark-3.0.1/spark-3.0.1-bin-hadoop2.7.tgz\n",
    "!tar xf spark-3.0.1-bin-hadoop2.7.tgz\n",
    "!pip install -q findspark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xhZxUoaSmabP"
   },
   "source": [
    "### Configuring Environment Variable for the Java and Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tUVJSa06xunP"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
    "os.environ[\"SPARK_HOME\"] = \"/content/spark-3.0.1-bin-hadoop2.7\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YpUY2lZCmwWD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J9pnFzBgmxej"
   },
   "source": [
    "### Importing findspark and SparkSession (which will be used during Model Processing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iDnCgT-iykIV"
   },
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "78tnav8EyuDV"
   },
   "outputs": [],
   "source": [
    "import pandas as pd    \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TPYowo4onJn-"
   },
   "source": [
    "### Importing Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mZIZJN0xipWz"
   },
   "outputs": [],
   "source": [
    "data = spark.read.csv(\"/content/drive/My Drive/Colab Notebooks/Credit_Card_Approval/application_record.csv\",inferSchema=True, header =True,encoding = 'utf-8') \n",
    "record = spark.read.csv(\"/content/drive/My Drive/Colab Notebooks/Credit_Card_Approval/credit_record.csv\",inferSchema=True, header =True, encoding = 'utf-8')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "86MNfPnCngu0"
   },
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dp8cKTmJyxwq"
   },
   "source": [
    "### Let us print look into the dataset to see the data types of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "_GWFvYJ1vkzT",
    "outputId": "342dfbfb-944f-4b53-9ffe-b84c08134197"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ID: integer (nullable = true)\n",
      " |-- CODE_GENDER: string (nullable = true)\n",
      " |-- FLAG_OWN_CAR: string (nullable = true)\n",
      " |-- FLAG_OWN_REALTY: string (nullable = true)\n",
      " |-- CNT_CHILDREN: integer (nullable = true)\n",
      " |-- AMT_INCOME_TOTAL: double (nullable = true)\n",
      " |-- NAME_INCOME_TYPE: string (nullable = true)\n",
      " |-- NAME_EDUCATION_TYPE: string (nullable = true)\n",
      " |-- NAME_FAMILY_STATUS: string (nullable = true)\n",
      " |-- NAME_HOUSING_TYPE: string (nullable = true)\n",
      " |-- DAYS_BIRTH: integer (nullable = true)\n",
      " |-- DAYS_EMPLOYED: integer (nullable = true)\n",
      " |-- FLAG_MOBIL: integer (nullable = true)\n",
      " |-- FLAG_WORK_PHONE: integer (nullable = true)\n",
      " |-- FLAG_PHONE: integer (nullable = true)\n",
      " |-- FLAG_EMAIL: integer (nullable = true)\n",
      " |-- OCCUPATION_TYPE: string (nullable = true)\n",
      " |-- CNT_FAM_MEMBERS: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "m9x_B9adwODZ",
    "outputId": "82176f17-6ddc-49e9-eb5a-3bdb81dd6e31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ID: integer (nullable = true)\n",
      " |-- MONTHS_BALANCE: integer (nullable = true)\n",
      " |-- STATUS: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "record.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yrBu4CiqwSDL"
   },
   "outputs": [],
   "source": [
    "df = data.toPandas()\n",
    "df_r = record.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "Yg7pOF5gzE30",
    "outputId": "ced5b0c0-cd97-40b4-f022-d7adf7b6b161"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Application dataset is :  (438557, 18)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008804</td>\n",
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       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5008805</td>\n",
       "      <td>M</td>\n",
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       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008806</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008809</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER  ... OCCUPATION_TYPE CNT_FAM_MEMBERS\n",
       "0  5008804           M  ...            None             2.0\n",
       "1  5008805           M  ...            None             2.0\n",
       "2  5008806           M  ...  Security staff             2.0\n",
       "3  5008808           F  ...     Sales staff             1.0\n",
       "4  5008809           F  ...     Sales staff             1.0\n",
       "\n",
       "[5 rows x 18 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Shape of Application dataset is : \", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 213
    },
    "id": "kCSkJ2aLzdbY",
    "outputId": "682122ee-d08e-4ed8-9ce7-0ce2440913a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Credit dataset is :  (1048575, 3)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5001711</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5001712</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        ID  MONTHS_BALANCE STATUS\n",
       "0  5001711               0      X\n",
       "1  5001711              -1      0\n",
       "2  5001711              -2      0\n",
       "3  5001711              -3      0\n",
       "4  5001712               0      C"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Shape of Credit dataset is : \", df_r.shape)\n",
    "df_r.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3OWMJVs40U7X"
   },
   "source": [
    "### How can we classify that the credit card user is not approiate enough to give further credits ?\n",
    "#### The Status shows how late the customer paid the credit card Bill.\n",
    "* 0: 1-29 days\n",
    "* 1: 30-59 days\n",
    "* 2: 60-89 days \n",
    "* 3: 90-119 days \n",
    "* 4: 120-149 days \n",
    "* 5: Overdue or bad debts write-offs for more than 150 days \n",
    "* C: paid off that month \n",
    "* X: No loan for the month\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "D6DeXUgHz90T",
    "outputId": "cac653dc-f773-454c-9385-468b5d4faab0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                          0\n",
       "CODE_GENDER                 0\n",
       "FLAG_OWN_CAR                0\n",
       "FLAG_OWN_REALTY             0\n",
       "CNT_CHILDREN                0\n",
       "AMT_INCOME_TOTAL            0\n",
       "NAME_INCOME_TYPE            0\n",
       "NAME_EDUCATION_TYPE         0\n",
       "NAME_FAMILY_STATUS          0\n",
       "NAME_HOUSING_TYPE           0\n",
       "DAYS_BIRTH                  0\n",
       "DAYS_EMPLOYED               0\n",
       "FLAG_MOBIL                  0\n",
       "FLAG_WORK_PHONE             0\n",
       "FLAG_PHONE                  0\n",
       "FLAG_EMAIL                  0\n",
       "OCCUPATION_TYPE        134203\n",
       "CNT_FAM_MEMBERS             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rTP2AdjhqKLX"
   },
   "source": [
    "Lets find out when the customer opened his account (Ex. -49 means opened account 49 months ago)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431
    },
    "id": "F4fTcf-_pNMI",
    "outputId": "14ee9c06-6345-43e6-e13e-191dfb83cab0"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
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       "      <td>-3</td>\n",
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       "      <th>5001712</th>\n",
       "      <td>-18</td>\n",
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       "    <tr>\n",
       "      <th>5001713</th>\n",
       "      <td>-21</td>\n",
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       "    <tr>\n",
       "      <th>5001714</th>\n",
       "      <td>-14</td>\n",
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       "      <th>5001715</th>\n",
       "      <td>-59</td>\n",
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       "      <td>-17</td>\n",
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       "      <th>5150484</th>\n",
       "      <td>-12</td>\n",
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       "      <td>-1</td>\n",
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       "      <th>5150487</th>\n",
       "      <td>-29</td>\n",
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       "<p>45985 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         MONTHS_BALANCE\n",
       "ID                     \n",
       "5001711              -3\n",
       "5001712             -18\n",
       "5001713             -21\n",
       "5001714             -14\n",
       "5001715             -59\n",
       "...                 ...\n",
       "5150482             -28\n",
       "5150483             -17\n",
       "5150484             -12\n",
       "5150485              -1\n",
       "5150487             -29\n",
       "\n",
       "[45985 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find all users' account open month. 0 = current month, -1 = previous month and so on..\n",
    "begin_month=pd.DataFrame(df_r.groupby([\"ID\"]).MONTHS_BALANCE.agg(\"min\"))\n",
    "begin_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ngtZbwoqXSI"
   },
   "source": [
    "Merged the record in the attribute Table (Left Join using ID as a common column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "3loEOQAZ1DSv",
    "outputId": "86c1a3a1-a2e2-40b0-af44-8537400a4698"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>starting_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>5008804</td>\n",
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       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
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       "      <td>5008806</td>\n",
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       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
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       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Sales staff</td>\n",
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       "    <tr>\n",
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       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
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       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER  ... CNT_FAM_MEMBERS starting_month\n",
       "0  5008804           M  ...             2.0          -15.0\n",
       "1  5008805           M  ...             2.0          -14.0\n",
       "2  5008806           M  ...             2.0          -29.0\n",
       "3  5008808           F  ...             1.0           -4.0\n",
       "4  5008809           F  ...             1.0          -26.0\n",
       "\n",
       "[5 rows x 19 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "begin_month=begin_month.rename(columns={'MONTHS_BALANCE':'starting_month'}) \n",
    "new_data=pd.merge(df,begin_month,how=\"left\",on=\"ID\") #merge to record data\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EoOIb9tYqsSz"
   },
   "source": [
    "We are assuming that if the customer paid very late payment (More than 60 days) We consider them as risky customer ( Might not pay the bill in future)\n",
    "\n",
    "So we are labeling all the customer with status 2,3,4,5 as Yes (Risk) others are Safe to give credit( Now by default it is None)\n",
    "\n",
    "New Column added as target_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "lwD38Em_1faL",
    "outputId": "3bc8053b-5562-4575-9c9b-968ef8419991"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "df_r['dep_value'] = None\n",
    "df_r['dep_value'][df_r['STATUS'] =='2']='Yes' \n",
    "df_r['dep_value'][df_r['STATUS'] =='3']='Yes' \n",
    "df_r['dep_value'][df_r['STATUS'] =='4']='Yes' \n",
    "df_r['dep_value'][df_r['STATUS'] =='5']='Yes' \n",
    "\n",
    "\n",
    "df_r.rename(columns= { \"dep_value\" : \"target_value\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GkPs5sHZr1Ui"
   },
   "source": [
    "Set None values as No or Yes depending other attirbute and merge the column with attribute table and Convert Yes and No into 1 and O."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "DS17hjfq6jYE",
    "outputId": "52373531-c81b-4372-9fd0-020bc6040dc1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
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       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>starting_month</th>\n",
       "      <th>target_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
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       "      <td>-14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008806</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008809</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER  ... starting_month target_value\n",
       "0  5008804           M  ...          -15.0            0\n",
       "1  5008805           M  ...          -14.0            0\n",
       "2  5008806           M  ...          -29.0            0\n",
       "3  5008808           F  ...           -4.0            0\n",
       "4  5008809           F  ...          -26.0            0\n",
       "\n",
       "[5 rows x 20 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df = df_r.groupby(\"ID\").count() #None - 0(No) otherwise NonZero(Yes)\n",
    "count_df[\"target_value\"][count_df[\"target_value\"] > 0] = \"Yes\"\n",
    "count_df[\"target_value\"][count_df[\"target_value\"] == 0] = \"No\"\n",
    "count_df = count_df[[\"target_value\"]]\n",
    "new_data = pd.merge(new_data, count_df, how=\"inner\", on=\"ID\")\n",
    "new_data.loc[new_data[\"target_value\"] == \"Yes\",\"target_value\"] = 1\n",
    "new_data.loc[new_data[\"target_value\"] == \"No\",\"target_value\"] = 0\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AExLlxunsJmG"
   },
   "source": [
    "How many Yes and No are in the Dataset (Seems like a skewed Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "id": "v_mXSbhE7sJd",
    "outputId": "888628ed-5e70-4005-e178-3b0cc812167e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     45318\n",
      "Yes      667\n",
      "Name: target_value, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No     0.985495\n",
       "Yes    0.014505\n",
       "Name: target_value, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(count_df['target_value'].value_counts())\n",
    "count_df['target_value'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4kNbgpxsRVJ"
   },
   "source": [
    "Renaming Columns for Clean Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c_oXMJ9qDUYp"
   },
   "outputs": [],
   "source": [
    "new_data.rename(columns={'CODE_GENDER':'Gender','FLAG_OWN_CAR':'Car','FLAG_OWN_REALTY':'Reality',\n",
    "                         'CNT_CHILDREN':'ChldNo','AMT_INCOME_TOTAL':'income',\n",
    "                         'NAME_EDUCATION_TYPE':'edutp','NAME_FAMILY_STATUS':'fam_status',\n",
    "                        'NAME_HOUSING_TYPE':'housingtp','FLAG_EMAIL':'email',\n",
    "                         'NAME_INCOME_TYPE':'incometp','FLAG_WORK_PHONE':'workphone',\n",
    "                         'FLAG_PHONE':'phone','CNT_FAM_MEMBERS':'familysize',\n",
    "                        'OCCUPATION_TYPE':'occytp'\n",
    "                        },inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kyWmjw5CMRJ9"
   },
   "source": [
    "Drop NULL Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "rWNKwqFNH6KC",
    "outputId": "a2626ad5-5f8e-485b-c7a4-840d6336f6ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                0\n",
       "Gender            0\n",
       "Car               0\n",
       "Reality           0\n",
       "ChldNo            0\n",
       "income            0\n",
       "incometp          0\n",
       "edutp             0\n",
       "fam_status        0\n",
       "housingtp         0\n",
       "DAYS_BIRTH        0\n",
       "DAYS_EMPLOYED     0\n",
       "FLAG_MOBIL        0\n",
       "workphone         0\n",
       "phone             0\n",
       "email             0\n",
       "occytp            0\n",
       "familysize        0\n",
       "starting_month    0\n",
       "target_value      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.dropna()\n",
    "new_data = new_data.mask(new_data == None).dropna()\n",
    "new_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6GOJ7W0MtKn"
   },
   "source": [
    "Create new table which will contain information value of each attribute so, we can idendify which attribute is more important for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KGvSYEbjIV7-"
   },
   "outputs": [],
   "source": [
    "ivtable=pd.DataFrame(new_data.columns,columns=['variable'])\n",
    "ivtable['IV']=None\n",
    "namelist = ['FLAG_MOBIL','starting_month','target_value','ID']\n",
    "for i in namelist:\n",
    "    ivtable.drop(ivtable[ivtable['variable'] == i].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "iaKND01mKHri",
    "outputId": "59c21491-b058-4c61-b406-1d7259d4f83d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gender</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reality</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ChldNo</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>incometp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>edutp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fam_status</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>housingtp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>workphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>phone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>email</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>occytp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>familysize</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable    IV\n",
       "1          Gender  None\n",
       "2             Car  None\n",
       "3         Reality  None\n",
       "4          ChldNo  None\n",
       "5          income  None\n",
       "6        incometp  None\n",
       "7           edutp  None\n",
       "8      fam_status  None\n",
       "9       housingtp  None\n",
       "10     DAYS_BIRTH  None\n",
       "11  DAYS_EMPLOYED  None\n",
       "13      workphone  None\n",
       "14          phone  None\n",
       "15          email  None\n",
       "16         occytp  None\n",
       "17     familysize  None"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivtable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9KbPkrbwPq7-"
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wfMVTQaKtAYb"
   },
   "source": [
    "Function to calculate the Information Value of Attribute(Column) depending on classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m4mgnIpCKdpj"
   },
   "outputs": [],
   "source": [
    "# Calculate information value\n",
    "def calc_iv(df, feature, target, pr=False):\n",
    "    lst = []\n",
    "    df[feature] = df[feature].fillna(\"NULL\")\n",
    "\n",
    "    for i in range(df[feature].nunique()):\n",
    "        val = list(df[feature].unique())[i]\n",
    "        lst.append([feature,                                                        # Variable\n",
    "                    val,                                                            # Value\n",
    "                    df[df[feature] == val].count()[feature],                        # All\n",
    "                    df[(df[feature] == val) & (df[target] == 0)].count()[feature],  # Good (think: Fraud == 0)\n",
    "                    df[(df[feature] == val) & (df[target] == 1)].count()[feature]]) # Bad (think: Fraud == 1)\n",
    "\n",
    "    data = pd.DataFrame(lst, columns=['Variable', 'Value', 'All', 'Good', 'Bad'])\n",
    "    data['Share'] = data['All'] / data['All'].sum()\n",
    "    data['Bad Rate'] = data['Bad'] / data['All']\n",
    "    data['Distribution Good'] = (data['All'] - data['Bad']) / (data['All'].sum() - data['Bad'].sum())\n",
    "    data['Distribution Bad'] = data['Bad'] / data['Bad'].sum()\n",
    "    data['WoE'] = np.log(data['Distribution Good'] / data['Distribution Bad'])\n",
    "    \n",
    "    data = data.replace({'WoE': {np.inf: 0, -np.inf: 0}})\n",
    "\n",
    "    data['IV'] = data['WoE'] * (data['Distribution Good'] - data['Distribution Bad'])\n",
    "\n",
    "    data = data.sort_values(by=['Variable', 'Value'], ascending=[True, True])\n",
    "    data.index = range(len(data.index))\n",
    "\n",
    "    if pr:\n",
    "        print(data)\n",
    "        print('IV = ', data['IV'].sum())\n",
    "\n",
    "    iv = data['IV'].sum()\n",
    "    print('This variable\\'s IV is:',iv)\n",
    "    print(df[feature].value_counts())\n",
    "    return iv, data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3RUdGCG4tNwZ"
   },
   "source": [
    "Function for coverting categorical varible to numerical values (Using multiple columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i-fII2SWSXOd"
   },
   "outputs": [],
   "source": [
    "def convert_dummy(df, feature,rank=0):\n",
    "    pos = pd.get_dummies(df[feature], prefix=feature)\n",
    "    mode = df[feature].value_counts().index[rank]\n",
    "    biggest = feature + '_' + str(mode)\n",
    "    pos.drop([biggest],axis=1,inplace=True)\n",
    "    df.drop([feature],axis=1,inplace=True)\n",
    "    df=df.join(pos)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FSKWeOw4Sp5C"
   },
   "outputs": [],
   "source": [
    "def get_category(df, col, binsnum, labels, qcut = False):\n",
    "    if qcut:\n",
    "        localdf = pd.qcut(df[col], q = binsnum, labels = labels) # quantile cut\n",
    "    else:\n",
    "        localdf = pd.cut(df[col], bins = binsnum, labels = labels) # equal-length cut\n",
    "        \n",
    "    localdf = pd.DataFrame(localdf)\n",
    "    name = 'gp' + '_' + col\n",
    "    localdf[name] = localdf[col]\n",
    "    df = df.join(localdf[name])\n",
    "    df[name] = df[name].astype(object)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aR4_LP17tZV5"
   },
   "source": [
    "Ploting Confusion Matrix for better visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5gtM_5H0SvtR"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        \n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hC7IwVYOttXF"
   },
   "source": [
    "# Binary Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QoAF_Ax8UIaA"
   },
   "source": [
    "#### Finding the IV score of all the unique values of Binary Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "XdtHhmDPTeFv",
    "outputId": "0f833378-145d-4a10-bfad-0b68d671fb4a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Gender', 'Car', 'Reality', 'ChldNo', 'income', 'incometp',\n",
       "       'edutp', 'fam_status', 'housingtp', 'DAYS_BIRTH', 'DAYS_EMPLOYED',\n",
       "       'FLAG_MOBIL', 'workphone', 'phone', 'email', 'occytp', 'familysize',\n",
       "       'starting_month', 'target_value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uZGn60DquE-z"
   },
   "source": [
    "Calculate Infomation Values for all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 943
    },
    "id": "Wg9x3ietUtGQ",
    "outputId": "b139fe3f-46a3-4506-916c-32d0f4fc0752"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.02520350452745081\n",
      "1    15630\n",
      "0     9504\n",
      "Name: Gender, dtype: int64\n",
      "This variable's IV is: 4.54248124999671e-06\n",
      "1    14618\n",
      "0    10516\n",
      "Name: Car, dtype: int64\n",
      "This variable's IV is: 0.02744070350168343\n",
      "0    16461\n",
      "1     8673\n",
      "Name: Reality, dtype: int64\n",
      "This variable's IV is: 0.002042429795148461\n",
      "1    18252\n",
      "0     6882\n",
      "Name: workphone, dtype: int64\n",
      "This variable's IV is: 0.0005480495762639297\n",
      "1    17775\n",
      "0     7359\n",
      "Name: phone, dtype: int64\n",
      "This variable's IV is: 1.7343581493999816e-05\n",
      "1    22604\n",
      "0     2530\n",
      "Name: email, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gender</td>\n",
       "      <td>0.0252035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car</td>\n",
       "      <td>4.54248e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reality</td>\n",
       "      <td>0.0274407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ChldNo</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>incometp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>edutp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fam_status</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>housingtp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>workphone</td>\n",
       "      <td>0.00204243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>phone</td>\n",
       "      <td>0.00054805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>email</td>\n",
       "      <td>1.73436e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>occytp</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>familysize</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable           IV\n",
       "1          Gender    0.0252035\n",
       "2             Car  4.54248e-06\n",
       "3         Reality    0.0274407\n",
       "4          ChldNo         None\n",
       "5          income         None\n",
       "6        incometp         None\n",
       "7           edutp         None\n",
       "8      fam_status         None\n",
       "9       housingtp         None\n",
       "10     DAYS_BIRTH         None\n",
       "11  DAYS_EMPLOYED         None\n",
       "13      workphone   0.00204243\n",
       "14          phone   0.00054805\n",
       "15          email  1.73436e-05\n",
       "16         occytp         None\n",
       "17     familysize         None"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_features = ['Gender', 'Car', 'Reality', 'workphone', 'phone', 'email']\n",
    "for i in binary_features:\n",
    "    new_data[i] = pd.get_dummies(new_data[i])\n",
    "    iv, data = calc_iv(new_data,i,'target_value')\n",
    "    ivtable.loc[ivtable['variable']==i,'IV']=iv\n",
    "ivtable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wEwZmGvmtqTY"
   },
   "source": [
    "# Continous Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zuxW92FVt0ve"
   },
   "source": [
    "Just make 3 groups for # of childs (0 , 1 ,2 or more than 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "id": "VdLg3gu6Vuax",
    "outputId": "09865017-9ffc-4d5a-84cb-0afdf8d1666e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            15908\n",
      "1             6118\n",
      "2 or More     3108\n",
      "Name: ChldNo, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data.loc[new_data['ChldNo'] >= 2,'ChldNo']='2 or More'\n",
    "print(new_data['ChldNo'].value_counts(sort=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 572
    },
    "id": "SNpB2kISXt4Q",
    "outputId": "47c42f9a-1ed6-401a-de82-547af52066cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.0011214542503301935\n",
      "0            15908\n",
      "1             6118\n",
      "2 or More     3108\n",
      "Name: ChldNo, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Car</th>\n",
       "      <th>Reality</th>\n",
       "      <th>income</th>\n",
       "      <th>incometp</th>\n",
       "      <th>edutp</th>\n",
       "      <th>fam_status</th>\n",
       "      <th>housingtp</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>workphone</th>\n",
       "      <th>phone</th>\n",
       "      <th>email</th>\n",
       "      <th>occytp</th>\n",
       "      <th>familysize</th>\n",
       "      <th>starting_month</th>\n",
       "      <th>target_value</th>\n",
       "      <th>ChldNo_1</th>\n",
       "      <th>ChldNo_2 or More</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008806</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008809</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5008810</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5008811</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Gender  Car  ...  target_value  ChldNo_1 ChldNo_2 or More\n",
       "2  5008806       0    0  ...             0         0                0\n",
       "3  5008808       1    1  ...             0         0                0\n",
       "4  5008809       1    1  ...             0         0                0\n",
       "5  5008810       1    1  ...             0         0                0\n",
       "6  5008811       1    1  ...             0         0                0\n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv, data=calc_iv(new_data,'ChldNo','target_value')\n",
    "ivtable.loc[ivtable['variable']=='ChldNo','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data, 'ChldNo')\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z1N0ngVGuYog"
   },
   "source": [
    "Converting Income to Categories (Requires Binning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 875
    },
    "id": "hV1EQkSGvlJM",
    "outputId": "c08581af-0300-445d-dc93-2e0525b2e12f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Car</th>\n",
       "      <th>Reality</th>\n",
       "      <th>income</th>\n",
       "      <th>incometp</th>\n",
       "      <th>edutp</th>\n",
       "      <th>fam_status</th>\n",
       "      <th>housingtp</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>workphone</th>\n",
       "      <th>phone</th>\n",
       "      <th>email</th>\n",
       "      <th>occytp</th>\n",
       "      <th>familysize</th>\n",
       "      <th>starting_month</th>\n",
       "      <th>target_value</th>\n",
       "      <th>ChldNo_1</th>\n",
       "      <th>ChldNo_2 or More</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008806</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008809</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5008810</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5008811</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36452</th>\n",
       "      <td>5149828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-17348</td>\n",
       "      <td>-2420</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Managers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36453</th>\n",
       "      <td>5149834</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-12387</td>\n",
       "      <td>-1325</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Medicine staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36454</th>\n",
       "      <td>5149838</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-12387</td>\n",
       "      <td>-1325</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Medicine staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36455</th>\n",
       "      <td>5150049</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>283500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-17958</td>\n",
       "      <td>-655</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36456</th>\n",
       "      <td>5150337</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-9188</td>\n",
       "      <td>-1193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25134 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender  Car  ...  target_value  ChldNo_1 ChldNo_2 or More\n",
       "2      5008806       0    0  ...             0         0                0\n",
       "3      5008808       1    1  ...             0         0                0\n",
       "4      5008809       1    1  ...             0         0                0\n",
       "5      5008810       1    1  ...             0         0                0\n",
       "6      5008811       1    1  ...             0         0                0\n",
       "...        ...     ...  ...  ...           ...       ...              ...\n",
       "36452  5149828       0    0  ...             1         0                0\n",
       "36453  5149834       1    1  ...             1         0                0\n",
       "36454  5149838       1    1  ...             1         0                0\n",
       "36455  5150049       1    1  ...             1         0                0\n",
       "36456  5150337       0    1  ...             1         0                0\n",
       "\n",
       "[25134 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "MkYiUxSVX39l",
    "outputId": "04e90043-c166-4c2f-d784-1131ce8658c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2.544, 18.18]      14663\n",
      "(18.18, 33.66]       8464\n",
      "(33.66, 49.14]       1637\n",
      "(49.14, 64.62]        175\n",
      "(64.62, 80.1]         124\n",
      "(80.1, 95.58]          50\n",
      "(95.58, 111.06]         4\n",
      "(111.06, 126.54]        3\n",
      "(126.54, 142.02]        6\n",
      "(142.02, 157.5]         8\n",
      "Name: income, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4f86891fd0>"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data['income']=new_data['income'].astype(object)\n",
    "new_data['income'] = new_data['income']/10000 \n",
    "print(new_data['income'].value_counts(bins=10,sort=False))\n",
    "new_data['income'].plot(kind='hist',bins=50,density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vyDtVcQHuz5l"
   },
   "source": [
    "Categorize the income into 3 groups low , medium , high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "gZzhwxaoZFH0",
    "outputId": "05aabf7f-1dcd-42b0-9c32-0d86e75ecc4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.0024219962221596752\n",
      "low       8996\n",
      "high      8244\n",
      "medium    7894\n",
      "Name: gp_income, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data = get_category(new_data,'income', 3, [\"low\",\"medium\", \"high\"], qcut = True)\n",
    "iv, data = calc_iv(new_data,'gp_income','target_value')\n",
    "ivtable.loc[ivtable['variable']=='income','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'gp_income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S9V3c14lv2TC"
   },
   "source": [
    "Convering Days into Years and Binning on age (Analysis Purposes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "a8pP9joGZ94a",
    "outputId": "2e8debd3-87d9-4274-c699-404c472cc76d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19.951999999999998, 24.7]    0.025066\n",
      "(24.7, 29.4]                  0.134280\n",
      "(29.4, 34.1]                  0.169770\n",
      "(34.1, 38.8]                  0.140805\n",
      "(38.8, 43.5]                  0.173072\n",
      "(43.5, 48.2]                  0.141880\n",
      "(48.2, 52.9]                  0.099069\n",
      "(52.9, 57.6]                  0.076550\n",
      "(57.6, 62.3]                  0.032585\n",
      "(62.3, 67.0]                  0.006923\n",
      "Name: Age, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4f86891f28>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data['Age']=-(new_data['DAYS_BIRTH'])//365\t\n",
    "print(new_data['Age'].value_counts(bins=10,normalize=True,sort=False))\n",
    "new_data['Age'].plot(kind='hist',bins=20,density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hKK5LfQTwbtu"
   },
   "source": [
    "After analysing the Graph we can conclude that size of 5 for category would be good balance for the model (Generalization with minimizing infomation loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yTyfYr-iwwbo"
   },
   "source": [
    "Calculating IV Values and Using Premade function to label the Age Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 140
    },
    "id": "CPI7F04iZ-yl",
    "outputId": "870bbf98-28a1-4de6-d053-e737fa4b57b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.06593513858884348\n",
      "medium     7916\n",
      "low        7806\n",
      "high       4414\n",
      "lowest     4005\n",
      "highest     993\n",
      "Name: gp_Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data = get_category(new_data,'Age',5, [\"lowest\",\"low\",\"medium\",\"high\",\"highest\"])\n",
    "iv, data = calc_iv(new_data,'gp_Age','target_value')\n",
    "ivtable.loc[ivtable['variable']=='DAYS_BIRTH','IV'] = iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'gp_Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XFOQnG4BxPcu"
   },
   "source": [
    "Same for the Work Experience (Fill nan values with mean values : Handling Missing/Errogenous Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "zRM-lieFaIT0",
    "outputId": "6ab51544-e415-4db9-a19f-bef99ac674bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4f8d822550>"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data['worktm']=-(new_data['DAYS_EMPLOYED'])//365\t\n",
    "new_data[new_data['worktm']<0] = np.nan\n",
    "new_data['DAYS_EMPLOYED']\n",
    "new_data['worktm'].fillna(new_data['worktm'].mean(),inplace=True) \n",
    "new_data['worktm'].plot(kind='hist',bins=20,density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xm9cNnge5WT_"
   },
   "source": [
    "Grouping into 5 Categories (Work Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 140
    },
    "id": "UtJKsUu-aKcG",
    "outputId": "28d3f782-d1cc-4d75-dadc-9d4940609340"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.04022152230816303\n",
      "lowest     18254\n",
      "low         4987\n",
      "medium      1378\n",
      "high         425\n",
      "highest       90\n",
      "Name: gp_worktm, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data = get_category(new_data,'worktm',5, [\"lowest\",\"low\",\"medium\",\"high\",\"highest\"])\n",
    "iv, data=calc_iv(new_data,'gp_worktm','target_value')\n",
    "ivtable.loc[ivtable['variable']=='DAYS_EMPLOYED','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'gp_worktm')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cwFgW2Q-x2da"
   },
   "source": [
    "For Family Size : Convert into 3 group ( 1 , 2 , 3 or more) and calculate Information Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "hiB_xGTjaXt6",
    "outputId": "0b44e346-0855-4fd1-da91-5dd1ccc0782a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.006156138510778323\n",
      "2        12697\n",
      "3more     8174\n",
      "1         4263\n",
      "Name: famsizegp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data['familysize']=new_data['familysize'].astype(int)\n",
    "new_data['famsizegp']=new_data['familysize']\n",
    "new_data['famsizegp']=new_data['famsizegp'].astype(object)\n",
    "new_data.loc[new_data['famsizegp']>=3,'famsizegp']='3more'\n",
    "iv, data=calc_iv(new_data,'famsizegp','target_value')\n",
    "ivtable.loc[ivtable['variable']=='familysize','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'famsizegp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xqjwIdUW5eWD"
   },
   "source": [
    "Convert Pensioner and Student to State Servant\n",
    "\n",
    "Calculate IV values for the Income Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "id": "-hbuDh_CaeFe",
    "outputId": "d2799667-f3ff-48ba-bd42-542d8d2653f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working                 0.621549\n",
      "State servant           0.096960\n",
      "Commercial associate    0.280576\n",
      "Pensioner               0.000517\n",
      "Student                 0.000398\n",
      "Name: incometp, dtype: float64\n",
      "This variable's IV is: 5.159303327851404e-05\n",
      "Working                 15622\n",
      "Commercial associate     7052\n",
      "State servant            2460\n",
      "Name: incometp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(new_data['incometp'].value_counts(normalize=True,sort=False))\n",
    "new_data.loc[(new_data['incometp']=='Pensioner') | (new_data['incometp']=='Student'),'incometp']='State servant'\n",
    "iv, data=calc_iv(new_data,'incometp','target_value')\n",
    "ivtable.loc[ivtable['variable']=='incometp','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'incometp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ftFcBOks5qA7"
   },
   "source": [
    "Manualling Categorizing the Occupation in 3 categories (Labor work , office work , High Tech work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B6fqDTNA3pX-"
   },
   "outputs": [],
   "source": [
    "new_data.loc[new_data[\"occytp\"].isin([\"Cleaning staff\",'Cooking staff',\"Drivers\", \"Laborers\", 'Low-skill Laborers', 'Security staff','Waiters/barmen staff']),\"occytp\"] = 'Laborwk'\n",
    "new_data.loc[new_data[\"occytp\"].isin([\"Accountants\",'Core staff',\"HR staff\", \"Medicine staff\", 'Private service staff', 'Realty agents','Sales staff','Secretaries']),\"occytp\"] = 'officewk'\n",
    "new_data.loc[new_data['occytp'].isin(['Managers','High skill tech staff','IT staff']),'occytp']='hightecwk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "3yTGT0xqavYE",
    "outputId": "1e6fa2d7-8b35-4b7d-b08e-35fd964df527"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Laborwk      10496\n",
      "officewk     10183\n",
      "hightecwk     4455\n",
      "Name: occytp, dtype: int64\n",
      "This variable's IV is: 0.004820472062853304\n",
      "Laborwk      10496\n",
      "officewk     10183\n",
      "hightecwk     4455\n",
      "Name: occytp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(new_data['occytp'].value_counts())\n",
    "iv, data=calc_iv(new_data,'occytp','target_value')\n",
    "ivtable.loc[ivtable['variable']=='occytp','IV']=iv\n",
    "data.head()    \n",
    "new_data = convert_dummy(new_data,'occytp')     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YdUsLgz056GV"
   },
   "source": [
    "Housing Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158
    },
    "id": "t6KtFTkIa1x9",
    "outputId": "a5dee7eb-26ca-47ce-b3ed-796bf81de3ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.0073275026880227365\n",
      "House / apartment      22102\n",
      "With parents            1430\n",
      "Municipal apartment      812\n",
      "Rented apartment         439\n",
      "Office apartment         199\n",
      "Co-op apartment          152\n",
      "Name: housingtp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "iv, data=calc_iv(new_data,'housingtp','target_value')\n",
    "ivtable.loc[ivtable['variable']=='housingtp','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'housingtp')     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qD8ZpLSN5-oj"
   },
   "source": [
    "Education Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "id": "lGIzCuc9a_-e",
    "outputId": "cfcdd00c-0740-40ea-c24d-95603d9bcc92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.010361794017679489\n",
      "Secondary / secondary special    16808\n",
      "Higher education                  7146\n",
      "Incomplete higher                  993\n",
      "Lower secondary                    187\n",
      "Name: edutp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "new_data.loc[new_data['edutp']=='Academic degree','edutp']='Higher education'\n",
    "iv, data=calc_iv(new_data,'edutp','target_value')\n",
    "ivtable.loc[ivtable['variable']=='edutp','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'edutp') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3__md4o_6CSD"
   },
   "source": [
    "Family Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 140
    },
    "id": "mLKbZiI0bSg4",
    "outputId": "12d9e724-17b7-4187-f310-94a59e39ad05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This variable's IV is: 0.043137115423127946\n",
      "Married                 17509\n",
      "Single / not married     3445\n",
      "Civil marriage           2133\n",
      "Separated                1467\n",
      "Widow                     580\n",
      "Name: fam_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "iv, data=calc_iv(new_data,'fam_status','target_value')\n",
    "ivtable.loc[ivtable['variable']=='fam_status','IV']=iv\n",
    "data.head()\n",
    "new_data = convert_dummy(new_data,'fam_status')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SjKgkEsE6FvY"
   },
   "source": [
    "# Now analysing all the Attributes through IV table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kGBkkEeh6Wfq"
   },
   "source": [
    "As per table most important attributes are age group , family status , work experience , .....\n",
    "\n",
    "whereas Least Important Attributes are Car , Email availabily ,income type(surprisingly) , phone availibily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "FHsiHI9ZbZPJ",
    "outputId": "49f8a6df-1989-46ac-c7b4-0abbdb0540d7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>agegp</td>\n",
       "      <td>0.0659351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fam_status</td>\n",
       "      <td>0.0431371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>worktmgp</td>\n",
       "      <td>0.0402215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reality</td>\n",
       "      <td>0.0274407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gender</td>\n",
       "      <td>0.0252035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>edutp</td>\n",
       "      <td>0.0103618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>housingtp</td>\n",
       "      <td>0.0073275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>familysize</td>\n",
       "      <td>0.00615614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>occytp</td>\n",
       "      <td>0.00482047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>0.002422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>workphone</td>\n",
       "      <td>0.00204243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ChldNo</td>\n",
       "      <td>0.00112145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>phone</td>\n",
       "      <td>0.00054805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>incometp</td>\n",
       "      <td>5.1593e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>email</td>\n",
       "      <td>1.73436e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car</td>\n",
       "      <td>4.54248e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      variable           IV\n",
       "10       agegp    0.0659351\n",
       "8   fam_status    0.0431371\n",
       "11    worktmgp    0.0402215\n",
       "3      Reality    0.0274407\n",
       "1       Gender    0.0252035\n",
       "7        edutp    0.0103618\n",
       "9    housingtp    0.0073275\n",
       "17  familysize   0.00615614\n",
       "16      occytp   0.00482047\n",
       "5       income     0.002422\n",
       "13   workphone   0.00204243\n",
       "4       ChldNo   0.00112145\n",
       "14       phone   0.00054805\n",
       "6     incometp   5.1593e-05\n",
       "15       email  1.73436e-05\n",
       "2          Car  4.54248e-06"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ivtable=ivtable.sort_values(by='IV',ascending=False)\n",
    "ivtable.loc[ivtable['variable']=='DAYS_BIRTH','variable']='agegp'\n",
    "ivtable.loc[ivtable['variable']=='DAYS_EMPLOYED','variable']='worktmgp'\n",
    "ivtable.loc[ivtable['variable']=='inc','variable']='incgp'\n",
    "ivtable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zxfybYF6x9F"
   },
   "source": [
    "# Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n5m4ot3R7tVq"
   },
   "source": [
    "First Only Take Attributes which is required for the model to train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "txIvP0Ft7svs",
    "outputId": "c142049f-5a27-4be3-91af-6783e5cba5ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Gender', 'Car', 'Reality', 'income', 'DAYS_BIRTH',\n",
       "       'DAYS_EMPLOYED', 'FLAG_MOBIL', 'workphone', 'phone', 'email',\n",
       "       'familysize', 'starting_month', 'target_value', 'ChldNo_1',\n",
       "       'ChldNo_2 or More', 'gp_income_high', 'gp_income_medium', 'Age',\n",
       "       'gp_Age_high', 'gp_Age_highest', 'gp_Age_low', 'gp_Age_lowest',\n",
       "       'worktm', 'gp_worktm_high', 'gp_worktm_highest', 'gp_worktm_low',\n",
       "       'gp_worktm_medium', 'famsizegp_1', 'famsizegp_3more',\n",
       "       'incometp_Commercial associate', 'incometp_State servant',\n",
       "       'occytp_hightecwk', 'occytp_officewk', 'housingtp_Co-op apartment',\n",
       "       'housingtp_Municipal apartment', 'housingtp_Office apartment',\n",
       "       'housingtp_Rented apartment', 'housingtp_With parents',\n",
       "       'edutp_Higher education', 'edutp_Incomplete higher',\n",
       "       'edutp_Lower secondary', 'fam_status_Civil marriage',\n",
       "       'fam_status_Separated', 'fam_status_Single / not married',\n",
       "       'fam_status_Widow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LE5Gl37eMUpv"
   },
   "source": [
    "dataset contains all the independent variable with dependent variable as well (target_value) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gpivwKLk8Ecx"
   },
   "outputs": [],
   "source": [
    "dataset = new_data[['Gender','Reality','ChldNo_1', 'ChldNo_2 or More','workphone',\n",
    "              'gp_Age_high', 'gp_Age_highest', 'gp_Age_low',\n",
    "       'gp_Age_lowest','gp_worktm_high', 'gp_worktm_highest',\n",
    "       'gp_worktm_low', 'gp_worktm_medium','occytp_hightecwk', \n",
    "              'occytp_officewk','famsizegp_1', 'famsizegp_3more',\n",
    "       'housingtp_Co-op apartment', 'housingtp_Municipal apartment',\n",
    "       'housingtp_Office apartment', 'housingtp_Rented apartment',\n",
    "       'housingtp_With parents','edutp_Higher education',\n",
    "       'edutp_Incomplete higher', 'edutp_Lower secondary','fam_status_Civil marriage',\n",
    "       'fam_status_Separated','fam_status_Single / not married','fam_status_Widow','target_value']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zG4maZxE7PhZ"
   },
   "source": [
    "## Spliting Dataset into Training : 79% , Testing : 21%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qRNiIo4mMjfM"
   },
   "source": [
    "Spark MLlib only support spark dataframe for the models so we have convert our pandas dataframe into spark dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xsBtXgBWB_XA"
   },
   "outputs": [],
   "source": [
    "df = spark.createDataFrame(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x05ZOwGNM31N"
   },
   "source": [
    "Set the attributes list to the assembler (which is responsible for assembling all the features into one columns )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rp1I9-NG-0IA"
   },
   "outputs": [],
   "source": [
    "feature_columns = list(dataset.columns[:-1])# here we omit the final column\n",
    "\n",
    "\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "assembler = VectorAssembler(inputCols=feature_columns,outputCol=\"features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vundFA8oNDWg"
   },
   "source": [
    "Assembler tranforming dataframe to usable dataframe for models (merging features columns into one column with comma separation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 494
    },
    "id": "mgpHjRVK_fL9",
    "outputId": "41050dc3-4b3f-4b77-a344-4260b58e781c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+--------+----------------+---------+-----------+--------------+----------+-------------+--------------+-----------------+-------------+----------------+----------------+---------------+-----------+---------------+-------------------------+-----------------------------+--------------------------+--------------------------+----------------------+----------------------+-----------------------+---------------------+-------------------------+--------------------+-------------------------------+----------------+------------+--------------------+\n",
      "|Gender|Reality|ChldNo_1|ChldNo_2 or More|workphone|gp_Age_high|gp_Age_highest|gp_Age_low|gp_Age_lowest|gp_worktm_high|gp_worktm_highest|gp_worktm_low|gp_worktm_medium|occytp_hightecwk|occytp_officewk|famsizegp_1|famsizegp_3more|housingtp_Co-op apartment|housingtp_Municipal apartment|housingtp_Office apartment|housingtp_Rented apartment|housingtp_With parents|edutp_Higher education|edutp_Incomplete higher|edutp_Lower secondary|fam_status_Civil marriage|fam_status_Separated|fam_status_Single / not married|fam_status_Widow|target_value|            features|\n",
      "+------+-------+--------+----------------+---------+-----------+--------------+----------+-------------+--------------+-----------------+-------------+----------------+----------------+---------------+-----------+---------------+-------------------------+-----------------------------+--------------------------+--------------------------+----------------------+----------------------+-----------------------+---------------------+-------------------------+--------------------+-------------------------------+----------------+------------+--------------------+\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           1.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[4,6],[1.0,1.0])|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        1.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          1|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              1|               0|           0|(29,[0,4,5,14,15,...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        1.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          1|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              1|               0|           0|(29,[0,4,5,14,15,...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        1.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          1|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              1|               0|           0|(29,[0,4,5,14,15,...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        1.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          1|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              1|               0|           0|(29,[0,4,5,14,15,...|\n",
      "|   0.0|    0.0|     0.0|             0.0|      0.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          0|              0|                        0|                            0|                         0|                         0|                     0|                     1|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[14,22],[1.0,...|\n",
      "|   0.0|    0.0|     0.0|             0.0|      0.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              1|          0|              0|                        0|                            0|                         0|                         0|                     0|                     1|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[14,22],[1.0,...|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   0.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|      (29,[4],[1.0])|\n",
      "|   1.0|    1.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          1.0|             0|                0|            0|               0|               0|              1|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      1|                    0|                        0|                   0|                              0|               0|           0|(29,[0,1,4,8,14,2...|\n",
      "|   1.0|    1.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          1.0|             0|                0|            0|               0|               0|              1|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      1|                    0|                        0|                   0|                              0|               0|           0|(29,[0,1,4,8,14,2...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          1.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[0,4,8],[1.0,...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          1.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[0,4,8],[1.0,...|\n",
      "|   1.0|    0.0|     0.0|             0.0|      1.0|        0.0|           0.0|       0.0|          1.0|             0|                0|            0|               0|               0|              0|          0|              0|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[0,4,8],[1.0,...|\n",
      "|   0.0|    0.0|     0.0|             1.0|      1.0|        0.0|           0.0|       1.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              1|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[3,4,7,16],[1...|\n",
      "|   0.0|    0.0|     0.0|             1.0|      1.0|        0.0|           0.0|       1.0|          0.0|             0|                0|            0|               0|               0|              0|          0|              1|                        0|                            0|                         0|                         0|                     0|                     0|                      0|                    0|                        0|                   0|                              0|               0|           0|(29,[3,4,7,16],[1...|\n",
      "+------+-------+--------+----------------+---------+-----------+--------------+----------+-------------+--------------+-----------------+-------------+----------------+----------------+---------------+-----------+---------------+-------------------------+-----------------------------+--------------------------+--------------------------+----------------------+----------------------+-----------------------+---------------------+-------------------------+--------------------+-------------------------------+----------------+------------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "AllinOne = assembler.transform(df)\n",
    "AllinOne.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "Gd3C4ty5DVTW",
    "outputId": "31d8b273-5bdf-4f0f-c11c-4c6cb6894a20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Count: 19867\n",
      "Test Dataset Count: 5267\n"
     ]
    }
   ],
   "source": [
    "train, test = AllinOne.randomSplit([0.79, 0.21], seed = 2018)\n",
    "print(\"Training Dataset Count: \" + str(train.count()))\n",
    "print(\"Test Dataset Count: \" + str(test.count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5b_Ao-jyNTbq"
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ivRTbEGrDbaJ"
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "lr = LogisticRegression(featuresCol = 'features', labelCol = 'target_value', maxIter=10)\n",
    "lrModel = lr.fit(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qQDeK9-oN12h"
   },
   "source": [
    "Accuracy : 61.90 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oY3SYXZVDCzz"
   },
   "outputs": [],
   "source": [
    "evaluation_summary = lrModel.evaluate(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "H4G7-vC27cW_",
    "outputId": "d6bc5591-5381-42d9-cb23-598b5819a429"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set areaUnderROC: 0.6238041056238366\n"
     ]
    }
   ],
   "source": [
    "trainingSummary = lrModel.summary\n",
    "roc = trainingSummary.roc.toPandas()\n",
    "plt.plot(roc['FPR'],roc['TPR'])\n",
    "plt.ylabel('False Positive Rate')\n",
    "plt.xlabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "print('Training set areaUnderROC: ' + str(trainingSummary.areaUnderROC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XCQyNEXfFoyR"
   },
   "outputs": [],
   "source": [
    "pr = trainingSummary.pr.toPandas()\n",
    "plt.plot(pr['recall'],pr['precision'])\n",
    "plt.ylabel('Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfdNK3GPHZm4"
   },
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hUqCcvtwNgCI"
   },
   "source": [
    "Fitting Train and Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mwEDZm_kF0rU"
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(featuresCol = 'features', labelCol = 'target_value', maxDepth = 4)\n",
    "dtModel = dt.fit(train)\n",
    "predictions = dtModel.transform(test)\n",
    "train_prediction = dtModel.transform(train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DrMSkYMuNkGs"
   },
   "source": [
    "Evaulating both prediction\n",
    "1. Testing dataset : 98.45 %\n",
    "2. Training dataset : 98.30 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "2LsHOJQsIc4N",
    "outputId": "58e7a5c7-6ee4-47cb-b808-c1b2320d09a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9836719194987659"
      ]
     },
     "execution_count": 88,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "evaluator = MulticlassClassificationEvaluator(labelCol=\"target_value\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
    "accuracy = evaluator.evaluate(predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "9b0zUNXfLlRB",
    "outputId": "88b4356e-226b-40e5-d2a0-ea47547d9dd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9834398751698797"
      ]
     },
     "execution_count": 89,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "evaluator = MulticlassClassificationEvaluator(labelCol=\"target_value\", predictionCol=\"prediction\", metricName=\"accuracy\")\n",
    "train_accuracy = evaluator.evaluate(train_prediction)\n",
    "train_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "6MbMMftmEe_D",
    "outputId": "43660e9d-13a2-4ee2-a78c-f5a864d26059"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>probability</th>\n",
       "      <th>target_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9867201426024955, 0.013279857397504456]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9867201426024955, 0.013279857397504456]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9867201426024955, 0.013279857397504456]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9867201426024955, 0.013279857397504456]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9867201426024955, 0.013279857397504456]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5262</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9809820193637621, 0.019017980636237896]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5263</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9809820193637621, 0.019017980636237896]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5264</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9809820193637621, 0.019017980636237896]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5265</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9770590447536668, 0.022940955246333206]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5266</th>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.9809820193637621, 0.019017980636237896]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5267 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prediction                                 probability  target_value\n",
       "0            0.0  [0.9867201426024955, 0.013279857397504456]             0\n",
       "1            0.0  [0.9867201426024955, 0.013279857397504456]             0\n",
       "2            0.0  [0.9867201426024955, 0.013279857397504456]             0\n",
       "3            0.0  [0.9867201426024955, 0.013279857397504456]             0\n",
       "4            0.0  [0.9867201426024955, 0.013279857397504456]             0\n",
       "...          ...                                         ...           ...\n",
       "5262         0.0  [0.9809820193637621, 0.019017980636237896]             0\n",
       "5263         0.0  [0.9809820193637621, 0.019017980636237896]             0\n",
       "5264         0.0  [0.9809820193637621, 0.019017980636237896]             0\n",
       "5265         0.0  [0.9770590447536668, 0.022940955246333206]             0\n",
       "5266         0.0  [0.9809820193637621, 0.019017980636237896]             0\n",
       "\n",
       "[5267 rows x 3 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = predictions.select('prediction', 'probability', 'target_value').toPandas()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5aA3Xhgq8aSH"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, title):\n",
    "  # Clear Plot\n",
    "  plt.gcf().clear()\n",
    "\n",
    "  # Configure figure\n",
    "  fig = plt.figure(1)\n",
    "  \n",
    "  # Configure plot\n",
    "  classes = ['No Fraud', 'Fraud']\n",
    "  plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "  plt.title(title)\n",
    "  plt.colorbar()\n",
    "  tick_marks = np.arange(len(classes))\n",
    "  plt.xticks(tick_marks, classes, rotation=45)\n",
    "  plt.yticks(tick_marks, classes)\n",
    "\n",
    "  # Normalize and establish threshold\n",
    "  normalize=False\n",
    "  fmt = 'd'\n",
    "  thresh = cm.max() / 2.\n",
    "\n",
    "  # Iterate through the confusion matrix cells\n",
    "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "      plt.text(j, i, format(cm[i, j], fmt),\n",
    "               horizontalalignment=\"center\",\n",
    "               color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "  # Final plot configurations\n",
    "  plt.tight_layout()\n",
    "  plt.ylabel('True label')\n",
    "  plt.xlabel('Predicted label') \n",
    "  \n",
    "  # Display images\n",
    "  image = fig\n",
    "  \n",
    "  # Show plot\n",
    "  #fig = plt.show()\n",
    "  \n",
    "  # Save plot\n",
    "  fig.savefig(\"confusion-matrix.png\")\n",
    "\n",
    "  # Display Plot\n",
    "  display(image)\n",
    "  \n",
    "  # Close Plot\n",
    "  plt.close(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QaByJNk78ovC"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(data[\"prediction\"], data[\"target_value\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "id": "RPRjZNHGGQ3P",
    "outputId": "eb822d4a-48f5-47b6-c036-11afc1208c40"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VVWVYr6ys7K72mZtfytg3N93VNmzF6tlDa82xnE9bZu0SzCbr2FHu8fWzdhlmk33iV7NvU84gM8Ld3Z3BgwcbJgnD1VFvYmIiAJs2baJHjx53tO2SkhKaN2+ORqMhISHBMKm4ZcuWHD9+nPLyci5cuMAPP/wAQLt27Thz5gynT58GMNQghLBxCmg0itGHLbPIPNsxY8ZQWPj/czhff/114uPjGTp0KAkJCcyaNeuOtvv000+zYcMGQkNDOXHiBA0aNACgRYsWDB48mCFDhjB58mQ6deoEgLOzM/Pnz2fcuHGEh4ff8SwJIYRlKYCiGH/Yshrr2dYmn321pdb86S1tBNtUG9sI5uydxm5NY/Y24581bBzVuu72bIUQwhxsfeRqjIStEMLmKYqCg4N9p62ErRDCLtj7bAQJWyGEXbDzrJWwFULYAUVGtkIIUeMUsPl5tMZI2Aoh7IKMbIUQwgLsPGslbIUQduAv3mPMFknYCiFsnvRshRDCQux8YCthK4SwD9JGEEKImmYHV/UyRsJWCGHzpGcrhBAWIm0EIYSwADvPWglbIYQdkHm2QghR86RnK4QQFiIjWyGEsAA7z1oJWyGEHZDr2QohRM1TAAfp2QohRM2z84GthK0Qwh7U4qlfCxYsqPbgZs+eXSMFCSHEnykK2HkX4fZh26VLF0vWIYQQ1aq182zDw8NveH758mXq169f4wUJIcStKNx92JaVlfHMM89QXl6OXq9n0KBBvPTSS2RnZzN16lSKioro3Lkz77zzDk5OTpSXlzNjxgwyMzNxd3dnyZIltGrVCoCVK1cSFxeHRqNh9uzZ9O/fv9p9a4wVd+jQIYKDg3n88ccBOHr0KHPnzr3rgxZCCFMpXG0jGHsY4+TkxNq1a/n666/ZuHEju3fvJj09ncWLFzNq1Ci2bt1K48aNiYuLAyA2NpbGjRuzdetWRo0axeLFiwE4duwYiYmJJCYmsnr1aubNm4der69230bDdtGiRXz88ce4u7sD4Ofnx4EDB4wflRBCmJHyf9dHqO5hyjYaNmwIQGVlJZWVlSiKQlpaGoMGDQKu/lW/fft2AHbs2GH4K3/QoEH88MMPqKrK9u3bCQkJwcnJCR8fH9q0aUNGRka1+zYatgAtWrS48U0ak94mhBBmoSgKDhrjD1Po9XqGDRvGQw89xEMPPYSPjw+NGzfG0fFqV9XLywudTgeATqcz5J+joyOurq4UFhai0+nw8vIybFOr1RrecztGp361aNGCgwcPoigKFRUVrFu3jvbt25t0UEIIYS6mzPwqKCggIiLC8DwqKoqoqKgb1nFwcCAhIYELFy4wYcIETpw4Ye5Sb8lo2M6dO5eFCxei0+no378//fr1Y86cOZaoTQghDExpE3h4eBAfH2/S9ho3bsyDDz5Ieno6Fy5coLKyEkdHR3Jzc9FqtcDVEeu5c+fw8vKisrKSkpISmjRpglarJTc317AtnU5neM/tGO0HeHh48O6775KamkpaWhqLFy+mSZMmJh2MEEKYi6IYfxhTUFDAhQsXALhy5Qqpqam0b9+eBx98kC1btgCwYcMGAgMDAQgMDGTDhg0AbNmyhd69e6MoCoGBgSQmJlJeXk52djZZWVl07dq12n0bHdlmZ2ezcOFC0tPTURSFgIAAXnvtNXx8fIwfmRBCmIGigIMZziDLy8tj5syZ6PV6VFVl8ODBPPLII/j6+jJlyhSWLl2Kv78/I0aMACAyMpLp06cTFBSEm5sbS5YsAaBDhw48/vjjBAcH4+DgwJw5c3BwcKh230bDdtq0aTz99NNER0cDkJiYyNSpU4mNjb3b4xZCCJOZ43RdPz8/Nm7ceNNyHx8fw3Sv6zk7O7Ns2bJbbmv8+PGMHz/e5H0bbSNcvnyZsLAwHB0dcXR0ZNiwYZSVlZm8AyGEMAdzzLO1ptuObIuKigAYMGAAq1atIjg4GEVR+Pbbbxk4cKDFChRCCIVafD3biIgIFEVBVVUA1q9fb3hNURSmTZtW89UJIQSAotTeayPs2LHDknUIIUS17DxrTbue7W+//caxY8coLy83LAsLC6uxooQQ4nq1uo1wTXR0NHv37uX48eMMHDiQ5ORkunfvLmErhLAo+45aE2YjbNmyhbVr19KsWTP++7//m4SEBEpKSixRmxBCGJjr2gjWYnRk6+zsjEajwdHRkdLSUpo2bcq5c+csUZsQQgDXzhCz7TA1xmjYdunShQsXLjBixAgiIiJo0KAB3bp1s0RtQghhYOdZa9qFaACeeuop+vfvT2lpKX5+fjVdlxBC3EBj52l727DNzMy87ZsyMzPp3LlzjRRkiwL8W1O4P9raZZjF8d+O1JpjqU0URcHRQa4TfTsKtXie7VtvvXXbNymKwrp162qkICGEuBV7/1V027D99NNPLVmHEELcnskfkKk1XsqdMumkBiGEsKZrN3y0ZxK2Qgi7YNo8WhnZCiHEHasNI1ujPWdVVUlISDBcPPzs2bNGb9krhBBmZcItcWx9ZpjRsJ07dy7p6ekkJiYC0LBhQ+bNm1fjhQkhxPU0imL0YcuMhm1GRgZvvPEGzs7OALi5uVFRUVHjhQkhxDUK4KAYf9gyoz1bR0dH9Hq9YdpFQUEBGo29z3gTQtgbWx+5GmM0bEeOHMmECRPIz89nyZIlfPfdd0yePNkStQkhBHDterbWruLuGA3b0NBQOnfuTFpaGqqq8sEHH9C+fXtL1CaEEFfZwQ0djTEatmfPnqV+/fo88sgjNyzz9vau0cKEEOIaBdu/Xq0xRsP2+eefN3xdVlZGTk4Obdu2NcxOEEIIS7DzrDUetps2bbrheWZmJp9//nmNFSSEELei2PmNcf7yGWSdO3eWkxqEEBZVG84gMxq2a9asMXxdVVXFL7/8gqenZ40WJYQQN1BMvTaC7TIathcvXjR87eDgwMCBAxk0aFCNFiWEENer9SNbvV7PxYsXeeWVVyxVjxBC3FKtnWdbWVmJo6MjBw8etGQ9QghxS7X2DLIRI0awYcMG/Pz8eOGFFxg8eDANGjQwvP7YY49ZpEAhhFAUsPdbtBnt2ZaXl9OkSRP27t17w3IJWyGEJWnMMPXr3LlzzJgxg/z8fBRF4YknnuC5556jqKiIKVOmcObMGVq2bMnSpUtxc3NDVVUWLlzIrl27cHFx4a233jLc7HbDhg18+OGHAIwfP57w8PBq933bsM3Pz2fNmjV06NABRVFQ1f+/Arpp9wISQgjzMUfsODg4MHPmTDp37kxpaSnDhw+nb9++xMfH06dPH8aNG8eqVatYtWoV06dPJzk5maysLJKSkvjpp5+YO3cusbGxFBUVER0dzVdffYWiKERERBAYGIibm9tt933bgXlVVRUXL17k0qVLhv+/9rh+hoIQQtS0a7MRjD2M8fT0NIxMGzVqRLt27dDpdGzfvp2wsDAAwsLC2LZtG4BhuaIoBAQEcOHCBfLy8khJSaFv3764u7vj5uZG37592b17d7X7vu3Itnnz5kycONHEb4UQQtQgxbRrIxQUFBAREWF4HhUVRVRU1C3XzcnJ4ciRI9x///3k5+cbzh9o3rw5+fn5AOh0Ory8vAzv8fLyQqfT3bRcq9Wi0+mqre22YXt920AIIazp6sjWeNh6eHgQHx9vdL2LFy/y0ksv8dprr9GoUaMb96UoNdIqvW0b4ZNPPjH7zoQQ4k6Z6x5kFRUVvPTSSwwdOtTwQX/Tpk3Jy8sDIC8vDw8PD+DqiDU3N9fw3tzcXLRa7U3LdTodWq222v3eNmzd3d1Nq1wIISxAY8LDGFVVmTVrFu3atWP06NGG5YGBgWzcuBGAjRs38uijj96wXFVV0tPTcXV1xdPTk379+pGSkkJxcTHFxcWkpKTQr1+/avcttzIXQtg8U9sIxvz4448kJCTQsWNHhg0bBsDUqVMZN24ckydPJi4uDm9vb5YuXQrAwIED2bVrF0FBQdSvX59FixYBVwejL774IpGRkQBMmDDB6ABVUaU5a1TmL0do39Hf2mWYxfHfas+x1Ca17edy8vcj+Pub73hSfvyJLJoaXa97gxKz7tecZGQrhLAL9j67X8JWCGHz6sQNH4UQwhY42HnaStgKIexAzcx9tSQJWyGEzVOQnq0QQliEjGyFEKKmKdKzFUKIGidtBCGEsBA7H9hK2Aoh7IM57tRgTRK2QgibZ65rI1iThK0Qwi7YedZK2Aoh7IEibQQhhLAEGdkKIUQNUxT779macnFzUUskbfmOISHBdPbz5V/vvGXtcgTw/NgxtPb2JGxYqLVLsXnmuLuuNUnY1hF6vZ7JL03gw49WcijjF2LXf8GRX36xdll13sjnRpHwzXfWLsMuKCb8Y8skbOuI/fv20b69Lz4+Pjg5OTEi6km+2ZRg7bLqvH79BxhuLihu79r1bM1xw0drkbCtI86ePUOrVj6G5y1btuLMmTNWrEiIv8ZBUYw+bJlNf0Dm7+9Px44dDc9XrFhBq1atzLqPwMBA4uLiZHQhhI2z9TaBMTYdti4uLiQk3PpPXVVVUVUVjUYG56bw9m5JTk624fmZMzm0bNnSihUJYbracFscu0qqnJwcBg0axIwZMxgyZAjnzp3jjTfeICIigpCQEJYtW2ZYNzAwkIKCAgB+/vlnRo4cCUBhYSFjxowhJCSEWbNmUVduLtyjZ0+OHfudnJwcysvLif1yPSFD5BNwYT8UEx62zKZHtleuXDHc271Vq1a8+uqrnDp1irfffpuAgAAApkyZgru7O3q9nlGjRnH06FH8/Pxuu80VK1bwwAMPMHHiRHbu3ElcXJzROirKyzj+2xHzHJQVzZgxg3H/GEuVqhIeHo5zPU2tOC57Nv3ll9m/fx+FhUXc08qLFydMZPjw4dYu666ZfRqWHfRkjbHpsP1zGyEnJwdvb29D0AJs3ryZmJgYKisrOX/+PMePH682bPfv3090dDQADz/8MG5ubkbrqOfkTPuOtnkv+r+ifUd/BgwYWCuOpbaI/zoRgOO/HalVP5eTv9fAL3H7zlrbDttbadCggeHr7Oxs/v3vfxMXF4ebmxszZ86krKwMAAcHB0OL4NoyIYR9utomsO+0taue7Z9dvHiR+vXr4+rqyh9//EFycrLhtZYtW3L48GEAkpKSDMt79uzJpk2bANi1axfFxcWWLVoIcUdknq0V+fn50alTJx5//HGmTZvGAw88YHht4sSJLFq0iIiICBwcHAzLJ0yYwIEDBwgJCWHr1q14e3tbo3QhxF9k72GrqHXl4/i7kPlL7emn1bbeYG1R234uJ38/gr+/+Y7nQPphcGttdL2GV86Ydb/mZHc9WyFE3aMY/sd+SdgKIeyCnWetffdshRB1hAKKohh9GPPqq6/Sp08fhgwZYlhWVFTE6NGjeeyxxxg9erThQ3NVVXnzzTcJCgpi6NChZGZmGt6zYcMGHnvsMR577DE2bNhg0iFI2Aoh7II5PiCLiIhg9erVNyxbtWoVffr0ISkpiT59+rBq1SoAkpOTycrKIikpiQULFjB37lzgajhHR0cTExNDbGws0dHRJs1qkrAVQtgFc5yu27Nnz5tOZNq+fTthYWEAhIWFsW3bthuWK4pCQEAAFy5cIC8vj5SUFPr27Yu7uztubm707duX3bt3G9239GyFEPahhpq2+fn5eHp6AtC8eXPy8/MB0Ol0eHl5Gdbz8vJCp9PdtFyr1aLT6YzuR8JWCGHzFEy7B1lBQQERERGG51FRUURFRZm+HxN7v3dCwlYIYRdMiUAPDw/i4+P/0nabNm1KXl4enp6e5OXlGa5trdVqyc3NNayXm5uLVqtFq9Wyb98+w3KdTkevXr2M7kd6tkII+1BD11gMDAxk48aNAGzcuJFHH330huWqqpKeno6rqyuenp7069ePlJQUiouLKS4uJiUlhX79+hndj4xshRB2wDw3dJw6dSr79u2jsLCQAQMGMGnSJMaNG8fkyZOJi4vD29ubpUuXAjBw4EB27dpFUFAQ9evXZ9GiRQC4u7vz4osvEhkZCVy9BIC7u7vxI5DTdY2T03VFTattPxdzn657KCMTl2b3GF+x+LScriuEEHfFzk8hk7AVQtgFU9oItvxnuoStEMIumDIjS8JWCCHukq1fr9YYCVshhJ8AYqQAABBHSURBVF2w99viSNgKIWyegoxshRDCIuw8ayVshRB24P+uZ2vPJGyFEHbBzrNWwlYIYR/sPGslbIUQdsLO01bCVghh80y9nq0tk7AVQtgF+45aCVshhJ2w84GthK0Qwh7cxdXBbYSErRDC5ikKaOw7ayVshRD2QdoIQghhAXIhGiGEsAT7zloJWyGEfZCerRBC1LCrcxHsO20lbIUQ9sG+s1bCVghhH+w8ayVshRB2QJFrIwghhEXYedaisXYBQghRF8jIVghh8+SGj0IIYSHSsxVCCAuw76iVsBVC2As7T1sJWyGEHVDs/gwymY0ghLB5165na+xhiuTkZAYNGkRQUBCrVq2q2cKvI2ErhLAPigkPI/R6PfPnz2f16tUkJibyzTffcOzYsZqr+ToStkIIu6CY8I8xGRkZtGnTBh8fH5ycnAgJCWH79u0WqF56tiapKC/j5O9HrF2GWWgUas2x1Ca17edSVlZm1u051XMk65jx78/58+eZNWuW4XlUVBRRUVGG5zqdDi8vL8NzrVZLRkaGWWu9HQlbEwQEBFi7BCHqtA4dOpi0nr+/PwMGDKjhau6MtBGEEHWGVqslNzfX8Fyn06HVai2ybwlbIUSdcd9995GVlUV2djbl5eUkJiYSGBhokX1LG0EIUWc4OjoyZ84cxo4di16vZ/jw4Sa3KO6WoqqqapE9CSFEHSZtBCGEsAAJWyGEsAAJW3FLp06dsnYJQtQqErbiJiUlJSxbtoy3337b2qXUafv37yc2NtbaZQgzkbAVN2nQoAFjx47l/PnzrFixwtrl1FkajYb33nuPr7/+2tqlCDNwmDt37lxrFyFsi0ajYc+ePZw/f57U1FQKCwvp2bOntcuqM65NEPL29sbFxYX333+fhg0b0qVLFytXJu6GjGzFTRISEvjwww+JjIwkMjKSrKwsli5dau2y6gxFUVAUhU8++YT09HQee+wxFi5cyPr1661dmrgLclKDQFVVlOvu71RSUsLo0aO5//778fX1xdfXl+XLl7N06VImT55sxUprt2s/h6qqKnJzc0lISOBf//oXvr6+hIWF8fzzz6OqKk899ZS1SxV3QEa2whC0x48fp7S0lGbNmrF27Vqys7Np2LAhvXr1olmzZpw6dYqCggIrV1t7Xfs5aDQavL296dChA5WVlVRWVtK1a1cmTpzIvHnzSEpKsnKl4k5I2AoAsrKy+OCDD9i8eTN9+vQhKiqKuXPnkpmZyZYtW7hy5QqzZ8/Gw8PD2qXWaj/88AMzZswAoGnTpqxZs4bLly8D4OnpydChQ/Hz87NmieIOyem6wmDjxo0cPHiQgIAAunfvzs6dO0lOTkav1/PKK6/g7+9v7RJrnT+3cE6cOMGyZcvw8fFh2rRpvPrqq5SWluLk5MSRI0dYuXIlPj4+VqxY3CkJ2zouOTmZI0eO8PzzzwOwadMmUlNT6dmzJxEREQCUl5fj5ORkzTJrvbNnz+Lt7Y2qqpw+fZrly5fTokULpk2bxtGjR8nJyaFjx460bt3a2qWKOyRTv+qY60dSer2e7OxskpKSyM/PJyAggHvvvZcjR46wfv163Nzc8PX1pV69elauuvaqqqri1KlTREZG4uvrS9u2bXF1dcXX15fPP/+czMxMhg8fTrt27XBzc7N2ueIuSNjWIdcHbXFxMVVVVXTo0IFmzZqRlJREbm4u3bp1o6SkhMLCQp5++mkaNWpk5aprn+t/Doqi4O7ujre3N++++y6tW7embdu2eHh4kJmZyR9//EGPHj1o2LChlasWd0umftUh1/4D/+STT0hLS6OiooLw8HCGDBmCoii88847pKamcvbsWaKjo2nWrJmVK659rg/aLVu2cO7cOXr37k1ISAjOzs4sXLiQ4uJiCgsLyc7O5q233qJp06ZWrlqYg4xs65jPP/+cLVu2sGLFClJTU1mxYgVNmjQhODiYoKAgXF1dGTt2LG3atLF2qbXStaD99NNPiYmJoXXr1ixbtgxHR0dCQ0O55557SEtL45dffmHq1KnSo61FZGRby1VVVaHRXJ3hp9Pp8PHxYenSpXz55ZdUVVXxn//8h1GjRlFVVcXIkSMJDg62csW10/Uj2szMTPbu3cu6deuIiYlBo9Gwd+9e9Ho9kZGR9O/fn4qKCumV1zIyz7aWuxa0X3zxBYsXL6Zr166oqsru3buZMmUKPXr04OGHH2bVqlWUlJQgk1PMr6qqyhC0mzdvxsPDg9dff520tDS2bdvGpk2buO+++1i1ahWJiYlUVlZK0NZCEra1VFpaGt988w0ABw4cICkpiddffx03Nzfq169Py5Yt+emnn/jPf/5D8+bN+eqrr3B1db1hzqe4e+fPnzf8wtu5cyfr1q3D2dkZrVZLXl6eoR/r5eVF165d6d+/P46O8gdnbSRhWwvt3r2bt99+Gy8vLyorK9m8eTN5eXkcOXIEgEaNGtG+fXt+/vlnYmJiePLJJ/H09LRy1bXPzp07GT9+PAUFBWRmZjJjxgxCQ0Px8PBAVVX69u3LqVOneOGFF4iOjuaf//ynfChZi8lJDbVMamoqr776KitWrKBLly6UlZWxfft2Dh8+jIuLC3/729/o1KkTcHWe7eXLl2V6Vw1ITk7mo48+4oUXXmDAgAFcvnyZDz/8kG+++YbY2FiaNm1KVVUVeXl57Nmzh549e8qHYbWcjGxrkfLycg4dOoSvry8tW7akqKiI5557Dq1WS1hYGOXl5ezYsYOMjAwAHBwcJGhrQFFREePGjWPMmDEMGDCA06dPM2/ePMaMGcPgwYOZMGECOp0OjUaDl5cXw4cPl6CtAyRsaxEnJyeGDBnCgAEDWLhwIVFRUYSHh9O9e3c6duxIcHAwBQUFpKWlUV5ebu1yay13d3c++ugjVqxYwdGjR5k9ezZ+fn64u7vz8ssv06dPH8aMGcMff/xh7VKFBUknvpZp06YNjzzyCJcuXUKn09GjRw/g6tSjTp068cQTT9C8eXO51kENe/jhh9FoNISFhTF16lTD9DqNRsOkSZNwdHTkypUr1i5TWJD0bGupnJwctm7dysmTJwkNDTWErrCsPXv2sGDBAmJjY3F1daWyslJmG9RR0kawY7f6PanX6wFo1aoVgYGBtGvXji+++IKffvrJ0uUJoG/fvrz66qtERkZSVFQkQVuHyU/eTl1/RlJWVhaurq40btyYevXqGc4+atOmDQMGDKBevXp4e3tbueK6a+DAgVRUVDB69Gi++uorwz3GRN0ibQQ799lnn/HVV1/Rrl07cnNzWblyJQ0bNrzhz1U59dM2XLx4Ua7eVYdJG8HOlJaWGr4+cOAAMTExLFu2jEWLFuHn58eTTz7JlStXbvhzVYLWNkjQ1m0Stnbk9OnTfPDBB4Z5sq6urgQEBNCqVSvq1avH7Nmz6dixI9u2bbNypUKIP5OwtSMlJSVoNBq2bdvGkSNHcHd3Z8+ePXz//feGHmCzZs2orKy0cqVCiD+Tnq0duHDhAo0bNwbg999/JzExkbKyMsaMGcOpU6eYOHEiY8aMQa/Xk5SUxHvvvUfbtm2tXLUQ4noStjYuNTWVefPm0b9/f0JDQ/Hy8kJRFNavX095eTnPPvssf/zxBykpKRQXFxMeHk779u2tXbYQ4k8kbG3ckSNHeOKJJ6hXrx5Tp05l3bp1/OMf/+DEiRM0adKEgoICnnvuOVq0aGHtUoUQ1ZCerY3z9/dnw4YNODk50ahRIz7++GMUReHw4cNs3ryZTz75hDVr1lBeXi4X/hbChsnI1k5kZGQwevRoZs2aRUREBHq9nqNHj7Jnzx4effRRaR0IYeMkbO1IRkYGf//735k8eTLPPPOMtcsRQvwFcrquHenatStr1qwhMjISZ2dnIiMjrV2SEMJEMrK1Q7/88gsuLi60a9fO2qUIIUwkYSuEEBYgsxGEEMICJGyFEMICJGyFEMICJGyFEMICJGyFEMICJGyFyfz9/Rk2bBhDhgzhpZde4vLly3e8rZkzZ/Ldd98BMGvWLI4dO3bbdffu3cvBgwf/8j4CAwMpKCgwefn1unXr9pf2tXz5cj7++OO/9B5Rt0jYCpO5uLiQkJDAN998Q7169Vi/fv0Nr9/pdXQXLlyIr6/vbV/ft28fhw4duqNtC2Er5AwycUd69OjBr7/+yt69e3n//fdp3LgxJ0+e5Ntvv2Xx4sXs27eP8vJynnnmGZ588klUVWXBggXs2bOHFi1a3HCrnpEjRzJjxgzuu+8+kpOTWbJkCXq9niZNmrBw4ULWr1+PRqPh66+/5vXXX6ddu3a88cYbnD17FoDXXnuN7t27U1hYyLRp09DpdAQEBJh0YZ4XX3yR3NxcysrKePbZZ4mKijK8tmjRIvbs2UOzZs1YsmQJHh4enD59mnnz5lFYWIiLiwsLFiyQ61II06hCmCggIEBVVVWtqKhQX3jhBfWzzz5T09LS1Pvvv189ffq0qqqqun79enXFihWqqqpqWVmZGh4erp4+fVrdsmWLOmrUKLWyslLNzc1Vu3fvrm7evFlVVVX9r//6LzUjI0PNz89XBwwYYNhWYWGhqqqqumzZMnX16tWGOqZOnaru379fVVVVPXPmjDp48GBVVVV1wYIF6vLly1VVVdXvv/9e7dixo5qfn3/TcTzyyCOG5df2cfnyZTUkJEQtKChQVVVVO3bsqCYkJKiqqqrLly9X582bp6qqqj777LPqyZMnVVVV1fT0dHXkyJG3rFGIP5ORrTDZlStXGDZsGHB1ZBsZGcmhQ4e477778PHxAWDPnj38+uuvbNmyBbh6K59Tp06xf/9+QkJCcHBwQKvV0rt375u2n56eTo8ePQzbcnd3v2UdqampN/R4S0tLuXjxIvv37yc6OhqAhx9+GDc3N6PH9Omnn7J161YAzp07x6lTp2jSpAkajYbg4GAAhg0bxsSJE7l48SKHDh3in//8p+H95eXlRvchBEgbQfwF13q2f9agQQPD16qqMnv2bPr373/DOrt27TJbHVVVVcTExODs7HxX29m7dy+pqal8+eWX1K9fn5EjR1JWVnbLdRVFQVVVGjdufMvvgRDGyAdkwqz69evHF198QUVFBQAnT57k0qVL9OzZk82bN6PX68nLy2Pv3r03vTcgIIADBw6QnZ0NQFFREXD1FuAXL168YR+ffvqp4fmRI0cA6NmzJ5s2bQKuhntxcXG1tZaUlODm5kb9+vU5fvw46enphteqqqoMo/NNmzbRvXt3GjVqRKtWrdi8eTNw9RfL0aNH/9o3SNRZErbCrEaMGIGvry8REREMGTKEOXPmoNfrCQoKok2bNgQHB/PKK68QEBBw03s9PDyYP38+kyZNIjQ0lClTpgDwyCOPsHXrVoYNG8aBAweYNWsWhw8fZujQoQQHB/PFF18AMGHCBA4cOEBISAhbt27F29u72loHDBhAZWUljz/+OO++++4NNTVo0ICMjAyGDBlCWloaEyZMAOBf//oXcXFxhIaGEhISIreNFyaTq34JIYQFyMhWCCEsQMJWCCEsQMJWCCEsQMJWCCEsQMJWCCEsQMJWCCEsQMJWCCEs4H8BO1lDY4ZJAN4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm, \"Hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d0A3FsHhE-eO"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Credit_card_Classification.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}